Apparatus and method for detecting undesired residues in a sample

ABSTRACT

Detection arrangement and method for detecting presence of a residue in a sample by determining color values of the sample, associated with the L*a*b color model, where a value of a composite parameter Z is calculated as follows: Z=w L +w a a+w b b where w L , w a , and w b  are weighting factors having a value depending on said residue and said sample, and a determination is made whether or not said sample comprises more or less than a predetermined amount of said residue in dependence on said value of said composite parameter Z. In a preferred embodiment, the arrangement is used to detect antibiotic residues, e.g. penicillin-G, in food products, e.g. milk, or body fluids, e.g. blood, urine.

FIELD OF THE INVENTION

The present invention relates to a detection arrangement for detectingpresence of a residue in a sample, comprising a processor, a memory, adisplay, and a scanner, said memory, said display and said scanner beingarranged to communicate with said processor, said scanner being arrangedto generate light signals, to send said light signals to said sample, toreceive reflected light signals back from said sample, to convert saidreflected light signals into color signals and to send said colorsignals to said processor, said processor being operated by instructionsstored in said memory and being arranged to display at least one colorvalue on said display in accordance with said color signal, said atleast one color value being associated with L*a*b color model.

It is observed that the term “sample” is here to be understood in abroad sense. Preferably, it refers both to parts that are (were) fixedto a body of an animal, human being or other living organism (e.g.plants) and to loose parts like milk, blood, fluid from muscle tissue,honey and eggs. More examples will be given below.

PRIOR ART

The presence of certain residues, e.g. pesticides, antibiotics orhormones, in food and feed is a growing concern among consumers due tohealth-related problems and the increase of drug resistant bacteria.Antibiotics are not only applied as medication but also as antimicrobialgrowth promoting substances.

Antimicrobial residues might be present in e.g. body liquids, organs,muscle tissues, eggs and plant tissues, which are used for consumption.Antimicrobial residues might also be present in food products in whichsaid animal products are added as an ingredient. Examples of foodproducts are milk; meat of cow, pig, poultry and fish; seafood such asshrimps; liver; processed meat products such as sausages, ready to eatmeals, baby food vegetables and fruit. Antimicrobial residues might alsobe present in body liquids or animal tissues, which are suitable forexamination by for example food-inspection authorities or centralizedlaboratories. Examples are milk, blood, pre-urine obtained from thekidney, urine, fluid from muscle tissue and other organs.

It is well known that food products such as consumption meat, organs,milk, sea-food, animal body liquids and animal tissues may contain toohigh concentrations of antimicrobial residues. In most countries, suchas the countries of the European Union, Canada and the United States,Maximum Residue Levels (MRL) are regulated by legislation.

Test methods to detect antimicrobial residues in food products such asmicrobial inhibition tests (e.g. agar diffusion tests) or methods makinguse of selective binders (e.g. antibodies or tracers) have been knownfor a long time. Examples of microbiological test methods have beendescribed in GB-A-1467439, EP 0005891, DE 3613794, CA 2056581, EP0285792, U.S. Pat. No. 5,434,053 and U.S. Pat. No. 5,494,805.

These descriptions deal with, for instance, ready-to-use tests that makeuse of a test microorganism. The test microorganism is mostly (but notnecessarily) imbedded in an agar medium, which may contain an indicator,a buffer solution, nutrients and substances to change the sensitivityfor certain antimicrobial compounds in a positive or negative way. Theindicator may be a color indicator, which changes its color in case themicroorganism grows.

Examples of suitable test organisms are strains of Bacillus,Streptococcus or E. coli. In general the principle of these tests isthat when antibacterial compounds are present in a sample in aconcentration sufficient to inhibit the growth of the test organism thecolor of an acid/base or redox indicator will remain the same, whilewhen no inhibition occurs the growth of the test organism is accompaniedby the formation of acid or reduced metabolites that will change thecolor of the indicator.

The test may also be based on use of detecting molecules such asenzymes, antibodies, and complex formers. The detecting molecules may beattached to a color containing or color-generating molecule. A recipientmolecule with affinity for the detection molecule is attached to adevice (for instance, test tube or test strip). Upon absence of theanalyte the detecting molecule will bind to the recipient molecule anddevelop a color. Also, strips can be designed where the presence of theanalyte will result in the development of a color.

In the presence of an analyte, binding at the recipient site will nottake place. A capture molecule with affinity for the analyte may bepresent on another location on for instance the test strip. In thepresence of the analyte, the analyte-detection molecule-color generatingmolecule will bind to the capture region and develop a color.

The development of color and the difference in intensities between thecolors of the recipient site and the capture site will determine thetest result.

Until now, both tests with test organism and indicator and the testscontaining binding molecules were mostly read visually. However, visualreadings make detection of antimicrobial residues in samples (e.g. sometypes of milk such as individual cow milk, liver, urine, kidney, meatjuice, eggs, honey, feed) not easy to perform. The reading made by eyeis limited by the performance of the human eye and has limitation formaking objective readings. Other types of tests, known in i.e. medicalapplications, involve color-reading devices that require theestablishment of calibration curves using several referenceconcentrations of the analyte to be detected that are laborious toperform and do not yield an easily readable positive or negative resultbased on a given threshold value, but rather supply information on aconcentration or concentration range.

SUMMARY OF THE INVENTION

It is an object of the present invention to provide an arrangement todetect the presence of residues in samples, using a color measurement toindicate whether or not the amount of residues is above a certainpredetermined threshold.

To that end, the present invention relates to an arrangement as referredto at the outset that is characterized in that the processor as operatedby the instructions is arranged to calculate a value of a compositeparameter Z in accordance with a following equation:$Z = {\sum\limits_{x = 1}^{x = n}\quad{w_{x} \cdot x}}$

-   -   where w_(x) is a weighting factor having a value depending on        the residue and the sample and x is a color value depending on        the color model, and to determine whether or not the sample        comprises more or less than a predetermined amount of the        residue in dependence on the value of the composite parameter Z.        When using the L*a*b color model, the value of composite        parameter Z is expressed by the following equation:        Z=w _(L) .L+w _(a) .a+w _(b) b    -   where W_(L), w_(a), and w_(b) are weighting factors having a        value depending on said residue and said sample, and to        determine whether or not said sample comprises more or less than        a predetermined amount of said residue in dependence on said        value of said composite parameter Z.

By combining two or more of the color components in the L*a*b* model ina predetermined manner that depends on the type of residue and thesample, a much more accurate detection is possible than in the priorart.

The invention also relates to a method as claimed in claim 9, a computerprogram product according to claim 10, and a data carrier according toclaim 11.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be explained with reference to some drawings. Thedrawings are not intended to limit the scope of protection of thepresent invention but only to illustrate the invention. The inventionitself is only limited by the scope of the annexed claims.

FIG. 1 shows a block diagram of the arrangement that can be used tocarry out the present invention.

FIG. 2 a shows a microtiter plate comprising a plurality of samples tobe investigated.

FIG. 2 b shows a test tube rack comprising a plurality of samples to beinvestigated.

FIGS. 3 a, 3 b and 3 c show test results for color values L, a, and bfor different penicillin-G concentrations in milk, respectively.

FIG. 4 shows test results for color values L, a, and b and compositeparameter Z for different penicillinr-G concentrations in milk at threedifferent time intervals.

FIG. 5 shows test results for composite parameter Z for differentenrofloxacin concentrations in meat fluid.

FIG. 6 shows test results for composite parameter Z for differentamoxicillin concentrations in chicken meat fluid.

FIG. 7 shows test results for color values L, a, and b forconcentrations of color components in recycle streams.

DESCRIPTION OF THE PREFERRED EMBODIMENT

In FIG. 1, an overview is given of a detection arrangement that can beused to carry out the method according to the invention. The arrangementcomprises a processor 1 for carrying out arithmetic operations.

The processor 1 is connected to a plurality of memory components,including a hard disk 5, Read Only Memory (ROM) 7, Electrically ErasableProgrammable Read Only Memory (EEPROM) 9, and Random Access Memory (RAM)11. Not all of these memory types need necessarily be provided.Moreover, these memory components need not be located physically closeto the processor 1 but may be located remote from the processor 1.

The processor 1 is also connected to means for inputting instructions,data etc. by a user, like a keyboard 13, and a mouse 15. Other inputmeans, such as a touch screen, a track ball and/or a voice converter,known to persons skilled in the art may be provided too.

A reading unit 17 connected to the processor 1 is provided. The readingunit 17 is arranged to read data from and possibly write data on a datacarrier like a floppy disk 19 or a CDROM 21. Other data carriers may betapes, DVD, etc, as is known to persons skilled in the art.

The processor 1 is also connected to a printer 23 for printing outputdata on paper, as well as to a display 3, for instance, a monitor or LCD(Liquid Crystal Display) screen, or any other type of display known topersons skilled in the art.

The processor 1 may be connected to a communication network 27, forinstance, the Public Switched Telephone Network (PSTN), a Local AreaNetwork (LAN), a Wide Area Network (WAN), etc. by means of I/O means 25.The processor 1 may be arranged to communicate with other communicationarrangements through the network 27.

The processor 1 may be implemented as stand alone system, or as aplurality of parallel operating processors each arranged to carry outsubtasks of a larger computer program, or as one or more main processorswith several sub processors. Parts of the functionality of the inventionmay even be carried out by remote processors communicating withprocessor 1 through the network 27.

The processor 1 is also connected to a scanner 29, e.g., a HP 6300CScanjet. On top of the scanner one or more test tube racks or microtiterplates 31 can be located. Instead of microtiter plates, other types ofsample holding devices may be employed. Suitable examples are test tuberacks containing test tubes designed in such a fashion that the contentsare at least visible from the side with which they are placed on thescanner. Photographic images of said objects may also be placed on topof the scanner.

Alternatively, a device that can perform the same function as thescanner 29 may replace the scanner 29. Such a device may be a digitalphoto camera or video camera, a web cam apparatus or the like. Saiddevices may be arranged in such a manner that images from the object tobe located can be conveniently collected. Preferably, the object isplaced in a position above the lens of said device, for instance byusing a mounting construction and/or a carrying area of transparentmaterial such as a glass plate. The distance between the lens of saiddevice and the object to be located is preferably less than one meter,more preferably less than 0.5 meter, most preferably less than 0.1meter. Preferably a light source is installed in such a way that it willilluminate the object.

By carrying out certain functionalities by a central processor through aWAN such as the Internet, additional advantages can be realized. In thisway, all users will use the same and the latest software versions,irrespective of their localization. Thus, the risk that in some casesoutdated software is used, is circumvented. Drawbacks of using outdatedsoftware are e.g. the fact that the latest legislative requirements arenot incorporated, corrections with regard to deviating scanners 29 ormicrotiter plates 31 are not incorporated, and results obtained bydifferent users cannot be uniformly interpreted. Any person ororganization, e.g. the manufacturer of the test systems or a regulatoryinstitute, may operate the central processor. Thus, an additionaladvantage is that the manufacturer of the test systems or the regulatoryinstitute can equip test systems with individual codes that are e.g.related to the production batch so that specifically tailored programson the central processor can be accessed using the code. Preferably,access to said central processor is achieved using the Internet by meansof personalized access code systems that are well known to the personskilled in the art. Alternatively, objects can be scanned orphotographed by a user and the digital or analogous image resulting fromthis scan or photograph can then be send in various ways, i.e. byelectronic mail, to the manufacturer of the test systems, a regulatoryinstitute or others for further processing such as calibration,measurement or the like.

FIG. 2 a shows a top view of an example of a microtiter plate 31 with 96cups 33 to contain samples to be tested. FIG. 2 b shows a bottom view ofan example of a test tube rack 31 with 50 ampoules 33 to contain samplesto be tested. The microtiter plate and test tube rack may be made ofplastic or any other suitable material known to persons skilled in theart.

The invention will now be explained in detail with reference to someexamples.

In a first embodiment (that is only intended as an example), theinvention relates to detecting analytes, for example residues ofantibiotics in milk. Nowadays, people use the Delvotest® to conduct sucha test. Another example is the BR®-test. Delvotest® is a commerciallyavailable test set, which comprises an agar matrix, comprising residuesof an acid forming microorganism, as well as a color indicator. First, atablet with nutrients is applied on the test set and, than, 100 μl ofthe milk to be tested. This is followed by an incubation time of 2.5hours with a temperature of 64° C. If there are no antibiotics (or onlylittle) that inhibit the growth of the test organism, after a certainamount of time, an acid environment is formed by the growingmicroorganisms. Then, the color of the indicator changes fromblue/purple to yellow. However, if there are sufficient antibiotics toinhibit that growth, the color of the indicator does not change andremains purple.

In a second embodiment, the invention relates to another suitable testfor detecting antibiotic residues in meat, the Premi®Test. Premi®Test isa commercially available test set comprising an agar matrix, whichcomprises residues of an acid forming microorganism, nutrients, as wellas a color indicator. First 100 μl of meat fluid to be tested is appliedon the agar matrix. This is followed by a pre-incubation of 20 minutesat 20±3° C. After the pre-incubation the meat fluid is rinsed withwater, preferably demineralized water, after which an additionalincubation of approximately 3 hours at 64° C. is performed.

The embodiments described above are indirect detection methods. The wordindirect in this respect refers to detection of a residue in a sample bymeans of visualization through one or more intermediate systems, i.e. amicroorganism and/or a product formed by said microorganism and/or acolor indicator displaying a color depending on the presence of saidproduct. The person skilled in the art will understand that the methodof the present invention is also suitably applicable for the directdetection of color components, for instance in recycle streams. The worddirect in this respect refers to detection of a residue in a sample bymeans of measuring the color of the residue itself.

With the arrangement shown in FIG. 1 it is possible to automaticallyscan the bottom side of each of the test samples in the test plate. Tothat end, the scanner 29 produces light that is directed to the testsamples in the test plate 31. Each of the test samples reflects thelight received.

The scanner receives the reflected light and sends signals with colorinformation of each of the scanned locations to the processor 1. Theprocessor 1 stores the signals with color information in one of hismemories, preferably, hard disk 5. This is all done automatically.Computer programs suitable to perform this function are available on themarket, as is known to persons skilled in the art.

The color and the brightness of the reflected light are registered inthree variables, each describing one color component with a color value.There are many different color models. However, most used color modelsare the RGB model (with a variable indicating the “amount” of anyone ofthe three colors red, green and blue, having color values R, G and B,respectively) and the so-called L*a*b* model. In the L*a*b* model, thecolor spectrum is divided in a two-dimensional matrix. The position of acolor in this matrix is registered by means of the two color values “a”(x-axis) and “b” (y-axis). The color value L indicates the intensity(for instance, from light blue to dark-blue). It is preferred to use theL*a*b* model since this model is also used in color measurements ofpowders. It has been established that some of the color values L, a, andb display a larger contribution to the power of discernment of certaintests than others. For instance, when detecting residues of antibioticsin milk using the BR®-test or the Delvotest®, color value L can beomitted without significant loss in the power of discernment.Surprisingly, it has been established that this leads to improved testresults when irregularities with the milk to be tested occur. Suchirregularities may be the presence of milk below the test medium ratherthan above, the presence of milk outside the bottom of the test tube andthe like. Likewise, when detecting residues of antibiotics in meat usingPremi®Test, color value L may also be omitted.

The following procedure may be followed to obtain a very good colorcalibration. In order to let the measured (scanned) colors correspondwith true colors, a scanner is to be calibrated. This can be done byscanning a reference object having known colors. Then, it is possible todetermine a systematic deviation of an individual scanner and to correctcolors in future scans. A suitable reference object is “Kodak Q-60 ColorInput Target”. This Q-60 target contains 264 colors of which the exactcolor values can be downloaded via Internet. The Q-60 targets areobtainable worldwide. Said calibration method affords a good and simplesolution to the problem of conventional calibration procedures thatinvolve the generation of a calibration curve using several referenceconcentrations of the analyte to be detected, for instance by usinglook-up tables, which are labor-intensive and do not in all casesprovide decision values that correspond with those of traditional visualdeterminations. Furthermore, said calibration method will instruct eachindividual scanning device, in combination with the processor, tointerpret any given color with the same numerical values as anotherscanning device. Yet another advantage of the calibration methodaccording to the present invention, which is described in detail below,is that it can be performed only once for each individual scanningdevice. This can be performed at the location where the scanning deviceis to be used but also at other locations. By using the method asdescribed below, calibration can be performed using a simple referenceobject such as, for instance, said “Kodak Q-60 Color Input Target”. Themethod allows for simple fixation of a single reference point below orabove which the result of a measurement is referred to as negative orpositive. For example, when testing for antibiotics such as penicillin-Gin samples such as milk, said reference point can be set at 4 ppb ofpenicillin-G, but every other value that serves a required threshold canalso be incorporated. The method of the present invention thus involvesa method that affords a variable threshold rather than a predeterminedthreshold.

According to one embodiment of the invention an optimal w_(L), w_(a),and w_(b) value can be determined such that the group means shows amaximal distance in relation to the chosen threshold value. Once theseoptimal values of weighting factors are determined with help ofreference samples, these values can be maintained for future testshaving the same threshold value. In general, w_(L), w_(a), and w_(b)values are not identical and preferably w_(a), and w_(b) are between 0.1and 0.9. By means of illustrating the above procedure, the determinationof optimal w_(L), w_(a), and w_(b) values for detecting penicillin-G inmilk is given below. The person skilled in the art will understand thata similar procedure can be followed for other types of detections.

All the samples on the test plates 31 were provided with milk withdifferent concentrations of penicillin-G. In a first test there were 8test plates provided with 48 samples with 100 μl milk and 48 sampleswith milk containing 4 ppb penicillin-G. In a second test there were 4test plates with 32 samples with milk only, 32 samples with milkcontaining 1.5 ppb penicillin-G and 32 samples with milk containing 2ppb penicillin-G.

The L, a, and b color values as measured of these 12 plates,respectively are indicated in FIGS. 3 a, 3 b and 3 c respectively. Everysample has a number: the last two digits refer to a cup (or sample)number on the plate concerned, whereas these last two numbers arepreceded by one or two digits referring to the plate concerned. E.g.,the 20^(th) sample on the 11th plate has the number 1120. This number ison the x-axis, whereas the L, a, and b color values respectively areindicated on the y-axis.

In a first embodiment, the test method is intended to detect thepresence of antibiotics in milk and not to provide a quantitativemeasurement of the concentration of antibiotics. In other words, thetest must classify each of the milk samples in either “negative” of“positive”, based on the measurement. A “negative” or “positive”decision refers to an amount of antibiotics below a certain thresholdvalue or above that threshold value, respectively.

FIGS. 3 a, 3 b and 3 c show that the separation between the measurementsof the concentrations 0 and 1.5 ppb is best for the a-values (FIG. 3 b),less with b-values (FIG. 3 c), and worst with L-values (FIG. 3 a).However, these figures also show that the separation between 1.5-2 ppbat the one hand and 4 ppb at the other hand is much better with theb-value. Thus, it can be concluded that the color values have anotherresolution. Moreover, this resolution depends on the concentration ofpenicillin-G.

FIG. 3 b also shows that one can define, for instance, a=3 as athreshold value. Based on the measurements, it is not to be expectedthat milk samples without antibiotics will have an a-value higher thanthis threshold. In other words, if one finds a sample with an a-valuehigher than 3, it is almost certain that there is a residue in the milk.

However, if one finds an a-value lower than 3, one cannot conclude thatthere is no residue at all in the milk sample tested. Then, the residueconcentration might, e.g., well be 1 ppb. If one finds an a-value lowerthan 3, one can conclude that the concentration of penicillin-G is mostprobably lower than 1.5 ppb.

In order to further improve the measurement method, the color valueswere listed against each other. In other words, a correlation patternbetween the color values was made. It turned out that the correlationwas dependent on the concentration of the residue. For instance, thegroup means of the L- and the a-values are negatively correlated.However, within the group of 0 ppb, the L- and a-values are uncorrelatedand show a more negative correlation with increasing concentrationtowards 4 ppb. L- and b-values are almost uncorrelated within the groupsbut positively correlated between the groups below 4 ppb. Group means ofthe b- and a-values are negatively correlated. Within the groups, the b-and a-values are only correlated at 1.5 ppb.

Consequently, one can conclude that the samples of the same residueconcentrations show almost no color differences and that the differencesbetween the measurement values will be mostly due to measurement noise.

FIG. 3 b shows that the a-value allows a good separation between residueconcentrations of 0 and 1.5 ppb or more. However, the a-valuemeasurements of the groups containing residue concentrations of 2 and 4ppb overlap strongly.

However, b-values show the opposite relationship. For the groups withresidue concentrations between 2 and 4 ppb there is no overlap of theb-value measurement results whereas there is overlap for the groupshaving residue concentrations between 0 and 1.5 ppb.

Therefore, it is possible to make a criterion comprising both colorvalues, i.e., both a-value and b-value. To be more general, it ispossible to use the a-value, b-value and L-value to make a compositefunction. In general, this composite function could be as follows:Z=w _(L) .L+w _(a) .a+w _(b) .b

-   -   where w_(L), w_(a) and w_(b) are weighting factors for the        L-value, a-value and b-value, respectively. The values of these        weighting factors can be calculated by means of “discriminent        analysis”, such that the group means show a maximum distance in        relation to the spreading.

For all data as shown in FIGS. 3 a, 3 b and 3 c it turns out that theoptimum function is:Z=0.35a−0.65h.

Of course, this optimum function is related to residues of penicillin-Gin milk. Other optimum functions will be found for other residues inother sample types. Actually, the optimum function may also differ forthe amount of penicillin-G in milk to be detected. For instance, if onewishes to distinguish residue concentration of 0, 1.5 and 2 ppb from oneanother, the optimum function may be:Z=0.70a−0.15b−0.15L

Thus, by varying the weighting factors, the amount of overlappingbetween the residue concentrations as measured is influenced in adifferent way.

In a second embodiment of the invention, quantitative measurements aremade: by means of an appropriate selection of the weighting factorw_(L), w_(a), and w_(b) it is possible to make a quantitativeexamination of residue concentrations in a certain range. For differentresidue concentration ranges, different values of the weighting factorswill have to be selected.

Although the examples given below relate to penicillin-G in milk,enrofloxacin in meat, amoxicillin in chicken meat, and color componentsin recycle streams, it will be evident to a person skilled in the artthat the method as explained is also applicable to β-lactam andquinolone antibiotics in general, but also to all other kinds of residueconcentration measurements in samples, as referred to in theintroduction of this description.

Thus, it has been shown that reliable and very simple to carry out, lowcost scanning technology from consumer electronics can be used toimprove current visual readings of diagnostic tests. Automaticreflection color analysis can be carried out very fast and improvessignificantly the assay performance in accuracy and the objectivity oftest results.

The invention is not limited to using light of a visible spectrum. It isemphasized that the arrangement will also give good results usinginfrared or ultraviolet.

EXAMPLES Example 1

The samples on plate 31 were provided with milk with differentconcentrations of penicillin-G, 0 ppb, 0.5 ppb, 1.0 ppb, 1.5 ppb, 2.0ppb, 2.5 ppb, 3.0 ppb, and 6.0 ppb, respectively. The L, a, b, andZ-color values as measured on these plates at three different timeintervals are displayed in FIG. 4.

Example 2

The samples on plate 31 were provided with meat tissue fluid withdifferent concentrations of enrofloxacin, 0 ppb, 19 ppb, 38 ppb, and 75ppb, respectively. In this example, the microorganism used is E. coli.The Z-color values as measured on these plates are displayed in FIG. 5.The Z-color values (y-axis) are measured during the time of theincubation (x-axis). The test method is designed to detect the presenceof antibiotics. In other words, the test must classify each of the meatfluid samples in either “negative” or “positive” based on the amount ofantibiotics below or above a certain threshold value, respectively. FIG.5 shows that the separation between the measurements of the enrofloxacinconcentrations 0 ppb, 19 ppb, 38 ppb, and 75 ppb increases with time.

Example 3

The samples on plate 31 were provided with chicken meat fluid withdifferent concentrations of amoxicillin. 25 Ampoules were provided with100 μl chicken meat fluid and 25 ampoules were provided with chickenmeat fluid containing 10 ppb amoxicillin. The Z-color values as measuredon this plate, are indicated in FIG. 6. The Z-color values (y-axis) aremeasured after an incubation time of 2 h50. Every sample is numbered anddisplayed on the x-axis. FIG. 6 shows the separation between themeasurements of “negative” chicken meat fluid and upositive chicken meatfluid (containing 10 ppb amoxicillin).

Example 4

This example relates to the detection of colored contaminants in recyclestreams. In the production of antibiotic intermediates by-products canbe formed which are of economic interest for process optimization. Forexample, a reactant can be recovered and recycled back to the process,reducing the raw materials cost. However, recycling of a target reactantmay generate a build-up of undesired impurities such as colorcomponents. Not only should these components be reduced during therecovery of the target reactant, additional color generation should beavoided during subsequent processing steps such as, for instance,sterilization. Hence, color after sterilization becomes a specificationfor the recycle stream and should be measured. In this case, the methodof the present invention is useful since it generates a quantitativemeasurement of the color, defined by L, a, and b color values. In therecycling of a reactant in a glucose-based fermentation process, asterilization or heat shock is carried out by mixing a glucose solutionwith a fresh reactant solution in ratios that vary between 20/80 and80/20. The mixture is heated up to 128° C. in a continuous system, witha residence time between 2 and 10 minutes.

On a smaller scale, the heat shock can be carried out batch wise, in anautoclave, as follows. With the recovered reactant, and depending on itscomposition, a reactant solution with the same final composition as afresh reactant solution is prepared. At least three reference mixtureswith glucose solution and fresh reactant solution are prepared at ratios20/80, 50/50 and 80/20. Three mixtures with glucose solution andrecovered reactant solution at the same ratios as the reference mixturesare prepared. After thorough agitation, the initial color is measuredaccording to the method of the present invention, at least twice permixture. All the mixtures are placed in the autoclave at 128° C. during10 minutes and the final color is measured according to the method ofthe present invention, at least twice per mixture. For a reactantrecovered by crystallization and recycled as 40% of the total reactantrequired, the L, a, and b values before and after heat shock arepresented in FIG. 7.

1. A detection arrangement for detecting presence of a residue in asample, comprising a processor, a display, and a scanner, said memory,said display and said scanner being arranged to communicate with saidprocessor, said scanner being arranged to generate light signals, tosend said light signals to said sample, to receive reflected lightsignals back from said sample, to convert said reflected light signalsinto color signals and to send said color signals to said processor,said processor being operated by instructions stored in said memory andbeing arranged to display at least one color value on said display inaccordance with said color signal, said at least one color value beingassociated with a color model characterized in that said processor asoperated by said instructions is arranged to calculate a value of acomposite parameter Z in accordance with a following equation:$Z = {\sum\limits_{x = 1}^{x = n}\quad{w_{x} \cdot x}}$ where w_(x) is aweighting factor having a value depending on said residue and saidsample and x is a color value depending on the color model, and todetermine whether or not said sample comprises more or less than apredetermined amount of said residue in dependence on said value of saidcomposite parameter Z.
 2. The detection arrangement of claim 1, whereinsaid color model is the L*a*b model and said equation is:Z=w _(L) .L+w _(a) .a+w _(b) .b where w_(L), w_(a), and w_(b) areweighting factors having a value depending on said residue and saidsample, and to determine whether or not said sample comprises more orless than a predetermined amount of said residue in dependence on saidvalue of said composite parameter Z.
 3. The detection arrangement ofclaim 1, wherein said composite parameter Z is calculated for optimalpower of discernment.
 4. The detection arrangement of claim 1, whereinsaid processor is connected to at least one remote processor through anetwork.
 5. The detection arrangement of claim 1, wherein said sample isa body liquid, an animal tissue or a food product.
 6. The detectionarrangement of claim 5, wherein said food product is selected from thegroup consisting of milk, eggs, cow meat, pig meat, poultry meat, fishmeat, sea food, and a processed meat product.
 7. The detectionarrangement of claim 5, wherein said body liquid is urine or blood. 8.The detection arrangement of claim 1, wherein said residue is apesticide or an antibiotic.
 9. The detection arrangement of claim 1,wherein said sample is milk, said residue is an antibiotic and saidequation is:Z=0.35.a−0.65.b
 10. The detection arrangement of claim 9, wherein saidmilk has a temperature of 64° C.
 11. The detection arrangement of claim1, wherein said processor as operated by said instructions, calculatesat least an absolute or a relative quantity of said residue in saidsample in dependence on said value of said composite parameter Z. 12.The detection arrangement of claim 1, wherein said scanner is calibratedwith a reference object having known colors.
 13. A method for detectingpresence of a residue in a sample by means of a detection arrangementcomprising a processor, a display, and a scanner, said memory, saiddisplay and said scanner being arranged to communicate with saidprocessor, the method including the steps by said scanner of generatinglight signals, sending said light signals to said sample, receivingreflected light signals back from said sample, converting said reflectedlight signals into color signals and sending said color signals to saidprocessor, displaying at least one color value on said display inaccordance with said color signal, said at least one color value beingassociated with a color model characterized in that said methodcomprises the steps by said processor of calculating a value of acomposite parameter Z in accordance with a following equation:$Z = {\sum\limits_{x = 1}^{x = n}\quad{w_{x} \cdot x}}$ where w_(x) is aweighting factor having a value depending on said residue and saidsample and x is a color value depending on the color model, anddetermining whether or not said sample comprises more or less than apredetermined amount of said residue in dependence on said value of saidcomposite parameter Z.
 14. The method of claim 13, wherein said colormodel is the L*a*b model and said equation is:Z=w _(L) .L+w _(a) .a+w _(b) .b where w_(L), w_(a), and w_(b) areweighting factors having a value depending on said residue and saidsample, and to determine whether or not said sample comprises more orless than a predetermined amount of said residue in dependence on saidvalue of said composite parameter Z.
 15. The method of claim 13, whereinsaid processor is connected to at least one remote processor through anetwork.
 16. The method of claim 13, wherein said composite parameter iscalculated for optimal power of discernment.
 17. The method of claim 13,wherein said scanner is calibrated with a reference object having knowncolors.
 18. A computer program product to be loaded by a detectionarrangement for detecting presence of a residue in a sample, comprisinga processor, a memory, a display, and a scanner, said memory, saiddisplay and said scanner being arranged to communicate with saidprocessor, said scanner being arranged to generate light signals, tosend said light signals to said sample, to receive reflected lightsignals back from said sample, to convert said reflected light signalsinto color signals and to send said color signals to said processor,said computer program product once loaded allowing said processor todisplay at least one color value on said display in accordance with saidcolor signal, said at least one color value being associated with acolor model characterized in that said computer program product onceloaded allows said processor to calculate a value of a compositeparameter Z in accordance with a following equation:$Z = {\sum\limits_{x = 1}^{x = n}\quad{w_{x} \cdot x}}$ where w_(x) is aweighting factor having a value depending on said residue and saidsample and x is a color value depending on the color model, and todetermine whether or not said sample comprises more or less than apredetermined amount of said residue in dependence on said value of saidcomposite parameter Z.
 19. The computer program of claim 18, whereinsaid color model is the L*a*b model and said equation is:Z=w _(L) .L+w _(a) .a+w _(b) .b where w_(L), w_(a), and w_(b) areweighting factors having a value depending on said residue and saidsample, and to determine whether or not said sample comprises more orless than a predetermined amount of said residue in dependence on saidvalue of said composite parameter Z.
 20. A data carrier comprising acomputer program product of claim
 18. 21. The use of a detectionarrangement of claim 1 for detecting the presence of a residue in asample.
 22. The detection arrangement of claim 9, wherein saidantibiotic is penicillin-G.
 23. A data carrier comprising a computerprogram product of claim 19.